{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### <font color = \"darkblue\">Updates to Assignment</font>\n",
    "\n",
    "#### If you were working on the older version:\n",
    "* Please click on the \"Coursera\" icon in the top right to open up the folder directory.  \n",
    "* Navigate to the folder: Week 3/ Planar data classification with one hidden layer.  You can see your prior work in version 6b: \"Planar data classification with one hidden layer v6b.ipynb\"\n",
    "\n",
    "#### List of bug fixes and enhancements\n",
    "* Clarifies that the classifier will learn to classify regions as either red or blue.\n",
    "* compute_cost function fixes np.squeeze by casting it as a float.\n",
    "* compute_cost instructions clarify the purpose of np.squeeze.\n",
    "* compute_cost clarifies that \"parameters\" parameter is not needed, but is kept in the function definition until the auto-grader is also updated.\n",
    "* nn_model removes extraction of parameter values, as the entire parameter dictionary is passed to the invoked functions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Planar data classification with one hidden layer\n",
    "\n",
    "Welcome to your week 3 programming assignment. It's time to build your first neural network, which will have a hidden layer. You will see a big difference between this model and the one you implemented using logistic regression. \n",
    "\n",
    "**You will learn how to:**\n",
    "- Implement a 2-class classification neural network with a single hidden layer\n",
    "- Use units with a non-linear activation function, such as tanh \n",
    "- Compute the cross entropy loss \n",
    "- Implement forward and backward propagation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 - Packages ##\n",
    "\n",
    "Let's first import all the packages that you will need during this assignment.\n",
    "- [numpy](https://www.numpy.org/) is the fundamental package for scientific computing with Python.\n",
    "- [sklearn](http://scikit-learn.org/stable/) provides simple and efficient tools for data mining and data analysis. \n",
    "- [matplotlib](http://matplotlib.org) is a library for plotting graphs in Python.\n",
    "- testCases provides some test examples to assess the correctness of your functions\n",
    "- planar_utils provide various useful functions used in this assignment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Package imports\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from testCases_v2 import *\n",
    "import sklearn\n",
    "import sklearn.datasets\n",
    "import sklearn.linear_model\n",
    "from planar_utils import plot_decision_boundary, sigmoid, load_planar_dataset, load_extra_datasets\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "np.random.seed(1) # set a seed so that the results are consistent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 - Dataset ##\n",
    "\n",
    "First, let's get the dataset you will work on. The following code will load a \"flower\" 2-class dataset into variables `X` and `Y`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X, Y = load_planar_dataset()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize the dataset using matplotlib. The data looks like a \"flower\" with some red (label y=0) and some blue (y=1) points. Your goal is to build a model to fit this data. In other words, we want the classifier to define regions as either red or blue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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toITNj86k/0cP1YFlNU9JkZVXnllCQb4Fa7kDo1FmztfxTJzaiYnXdMZsNnBg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\npZRyGQ3sSinlMhrYlVLKZTSwK6WUy/wfuoiIWc8YlM0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f17985d9390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the data:\n",
    "plt.scatter(X[0, :], X[1, :], c=Y, s=40, cmap=plt.cm.Spectral);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You have:\n",
    "    - a numpy-array (matrix) X that contains your features (x1, x2)\n",
    "    - a numpy-array (vector) Y that contains your labels (red:0, blue:1).\n",
    "\n",
    "Lets first get a better sense of what our data is like. \n",
    "\n",
    "**Exercise**: How many training examples do you have? In addition, what is the `shape` of the variables `X` and `Y`? \n",
    "\n",
    "**Hint**: How do you get the shape of a numpy array? [(help)](https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.shape.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of X is: (2, 400)\n",
      "The shape of Y is: (1, 400)\n",
      "I have m = 400 training examples!\n"
     ]
    }
   ],
   "source": [
    "### START CODE HERE ### (≈ 3 lines of code)\n",
    "shape_X = np.shape(X)\n",
    "shape_Y = np.shape(Y)\n",
    "m = 400  # training set size\n",
    "### END CODE HERE ###\n",
    "\n",
    "print ('The shape of X is: ' + str(shape_X))\n",
    "print ('The shape of Y is: ' + str(shape_Y))\n",
    "print ('I have m = %d training examples!' % (m))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "       \n",
    "<table style=\"width:20%\">\n",
    "  \n",
    "  <tr>\n",
    "    <td>**shape of X**</td>\n",
    "    <td> (2, 400) </td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**shape of Y**</td>\n",
    "    <td>(1, 400) </td> \n",
    "  </tr>\n",
    "  \n",
    "    <tr>\n",
    "    <td>**m**</td>\n",
    "    <td> 400 </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 - Simple Logistic Regression\n",
    "\n",
    "Before building a full neural network, lets first see how logistic regression performs on this problem. You can use sklearn's built-in functions to do that. Run the code below to train a logistic regression classifier on the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Train the logistic regression classifier\n",
    "clf = sklearn.linear_model.LogisticRegressionCV();\n",
    "clf.fit(X.T, Y.T);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can now plot the decision boundary of these models. Run the code below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of logistic regression: 47 % (percentage of correctly labelled datapoints)\n"
     ]
    },
    {
     "data": {
      "image/png": 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Jzqy/mf2Moa/1FtjFNNLL6Mew8yP0NRRPzL3KwwunSfkixMwMwVtUyxxbvsSp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\nBIVCoVDk+P8B2BAvSDnlqLAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f179863ff60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the decision boundary for logistic regression\n",
    "plot_decision_boundary(lambda x: clf.predict(x), X, Y)\n",
    "plt.title(\"Logistic Regression\")\n",
    "\n",
    "# Print accuracy\n",
    "LR_predictions = clf.predict(X.T)\n",
    "print ('Accuracy of logistic regression: %d ' % float((np.dot(Y,LR_predictions) + np.dot(1-Y,1-LR_predictions))/float(Y.size)*100) +\n",
    "       '% ' + \"(percentage of correctly labelled datapoints)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table style=\"width:20%\">\n",
    "  <tr>\n",
    "    <td>**Accuracy**</td>\n",
    "    <td> 47% </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Interpretation**: The dataset is not linearly separable, so logistic regression doesn't perform well. Hopefully a neural network will do better. Let's try this now! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 - Neural Network model\n",
    "\n",
    "Logistic regression did not work well on the \"flower dataset\". You are going to train a Neural Network with a single hidden layer.\n",
    "\n",
    "**Here is our model**:\n",
    "<img src=\"images/classification_kiank.png\" style=\"width:600px;height:300px;\">\n",
    "\n",
    "**Mathematically**:\n",
    "\n",
    "For one example $x^{(i)}$:\n",
    "$$z^{[1] (i)} =  W^{[1]} x^{(i)} + b^{[1]}\\tag{1}$$ \n",
    "$$a^{[1] (i)} = \\tanh(z^{[1] (i)})\\tag{2}$$\n",
    "$$z^{[2] (i)} = W^{[2]} a^{[1] (i)} + b^{[2]}\\tag{3}$$\n",
    "$$\\hat{y}^{(i)} = a^{[2] (i)} = \\sigma(z^{ [2] (i)})\\tag{4}$$\n",
    "$$y^{(i)}_{prediction} = \\begin{cases} 1 & \\mbox{if } a^{[2](i)} > 0.5 \\\\ 0 & \\mbox{otherwise } \\end{cases}\\tag{5}$$\n",
    "\n",
    "Given the predictions on all the examples, you can also compute the cost $J$ as follows: \n",
    "$$J = - \\frac{1}{m} \\sum\\limits_{i = 0}^{m} \\large\\left(\\small y^{(i)}\\log\\left(a^{[2] (i)}\\right) + (1-y^{(i)})\\log\\left(1- a^{[2] (i)}\\right)  \\large  \\right) \\small \\tag{6}$$\n",
    "\n",
    "**Reminder**: The general methodology to build a Neural Network is to:\n",
    "    1. Define the neural network structure ( # of input units,  # of hidden units, etc). \n",
    "    2. Initialize the model's parameters\n",
    "    3. Loop:\n",
    "        - Implement forward propagation\n",
    "        - Compute loss\n",
    "        - Implement backward propagation to get the gradients\n",
    "        - Update parameters (gradient descent)\n",
    "\n",
    "You often build helper functions to compute steps 1-3 and then merge them into one function we call `nn_model()`. Once you've built `nn_model()` and learnt the right parameters, you can make predictions on new data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 - Defining the neural network structure ####\n",
    "\n",
    "**Exercise**: Define three variables:\n",
    "    - n_x: the size of the input layer\n",
    "    - n_h: the size of the hidden layer (set this to 4) \n",
    "    - n_y: the size of the output layer\n",
    "\n",
    "**Hint**: Use shapes of X and Y to find n_x and n_y. Also, hard code the hidden layer size to be 4."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: layer_sizes\n",
    "\n",
    "def layer_sizes(X, Y):\n",
    "    \"\"\"\n",
    "    Arguments:\n",
    "    X -- input dataset of shape (input size, number of examples)\n",
    "    Y -- labels of shape (output size, number of examples)\n",
    "    \n",
    "    Returns:\n",
    "    n_x -- the size of the input layer\n",
    "    n_h -- the size of the hidden layer\n",
    "    n_y -- the size of the output layer\n",
    "    \"\"\"\n",
    "    ### START CODE HERE ### (≈ 3 lines of code)\n",
    "    n_x = np.ma.shape(X)[0] # size of input layer\n",
    "    n_h = 4\n",
    "    n_y = np.ma.shape(Y)[0] # size of output layer\n",
    "    ### END CODE HERE ###\n",
    "    return (n_x, n_h, n_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The size of the input layer is: n_x = 5\n",
      "The size of the hidden layer is: n_h = 4\n",
      "The size of the output layer is: n_y = 2\n"
     ]
    }
   ],
   "source": [
    "X_assess, Y_assess = layer_sizes_test_case()\n",
    "(n_x, n_h, n_y) = layer_sizes(X_assess, Y_assess)\n",
    "print(\"The size of the input layer is: n_x = \" + str(n_x))\n",
    "print(\"The size of the hidden layer is: n_h = \" + str(n_h))\n",
    "print(\"The size of the output layer is: n_y = \" + str(n_y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output** (these are not the sizes you will use for your network, they are just used to assess the function you've just coded).\n",
    "\n",
    "<table style=\"width:20%\">\n",
    "  <tr>\n",
    "    <td>**n_x**</td>\n",
    "    <td> 5 </td> \n",
    "  </tr>\n",
    "  \n",
    "    <tr>\n",
    "    <td>**n_h**</td>\n",
    "    <td> 4 </td> \n",
    "  </tr>\n",
    "  \n",
    "    <tr>\n",
    "    <td>**n_y**</td>\n",
    "    <td> 2 </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 - Initialize the model's parameters ####\n",
    "\n",
    "**Exercise**: Implement the function `initialize_parameters()`.\n",
    "\n",
    "**Instructions**:\n",
    "- Make sure your parameters' sizes are right. Refer to the neural network figure above if needed.\n",
    "- You will initialize the weights matrices with random values. \n",
    "    - Use: `np.random.randn(a,b) * 0.01` to randomly initialize a matrix of shape (a,b).\n",
    "- You will initialize the bias vectors as zeros. \n",
    "    - Use: `np.zeros((a,b))` to initialize a matrix of shape (a,b) with zeros."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: initialize_parameters\n",
    "\n",
    "def initialize_parameters(n_x, n_h, n_y):\n",
    "    \"\"\"\n",
    "    Argument:\n",
    "    n_x -- size of the input layer\n",
    "    n_h -- size of the hidden layer\n",
    "    n_y -- size of the output layer\n",
    "    \n",
    "    Returns:\n",
    "    params -- python dictionary containing your parameters:\n",
    "                    W1 -- weight matrix of shape (n_h, n_x)\n",
    "                    b1 -- bias vector of shape (n_h, 1)\n",
    "                    W2 -- weight matrix of shape (n_y, n_h)\n",
    "                    b2 -- bias vector of shape (n_y, 1)\n",
    "    \"\"\"\n",
    "    \n",
    "    np.random.seed(2) # we set up a seed so that your output matches ours although the initialization is random.\n",
    "    \n",
    "    ### START CODE HERE ### (≈ 4 lines of code)\n",
    "    W1 = np.random.randn(n_h, n_x) * 0.01\n",
    "    b1 = np.zeros((n_h,1))\n",
    "    W2 = np.random.randn(n_y, n_h) * 0.01\n",
    "    b2 = np.zeros((n_y, 1))\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    assert (W1.shape == (n_h, n_x))\n",
    "    assert (b1.shape == (n_h, 1))\n",
    "    assert (W2.shape == (n_y, n_h))\n",
    "    assert (b2.shape == (n_y, 1))\n",
    "    \n",
    "    parameters = {\"W1\": W1,\n",
    "                  \"b1\": b1,\n",
    "                  \"W2\": W2,\n",
    "                  \"b2\": b2}\n",
    "    \n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 = [[-0.00416758 -0.00056267]\n",
      " [-0.02136196  0.01640271]\n",
      " [-0.01793436 -0.00841747]\n",
      " [ 0.00502881 -0.01245288]]\n",
      "b1 = [[ 0.]\n",
      " [ 0.]\n",
      " [ 0.]\n",
      " [ 0.]]\n",
      "W2 = [[-0.01057952 -0.00909008  0.00551454  0.02292208]]\n",
      "b2 = [[ 0.]]\n"
     ]
    }
   ],
   "source": [
    "n_x, n_h, n_y = initialize_parameters_test_case()\n",
    "\n",
    "parameters = initialize_parameters(n_x, n_h, n_y)\n",
    "print(\"W1 = \" + str(parameters[\"W1\"]))\n",
    "print(\"b1 = \" + str(parameters[\"b1\"]))\n",
    "print(\"W2 = \" + str(parameters[\"W2\"]))\n",
    "print(\"b2 = \" + str(parameters[\"b2\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table style=\"width:90%\">\n",
    "  <tr>\n",
    "    <td>**W1**</td>\n",
    "    <td> [[-0.00416758 -0.00056267]\n",
    " [-0.02136196  0.01640271]\n",
    " [-0.01793436 -0.00841747]\n",
    " [ 0.00502881 -0.01245288]] </td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**b1**</td>\n",
    "    <td> [[ 0.]\n",
    " [ 0.]\n",
    " [ 0.]\n",
    " [ 0.]] </td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**W2**</td>\n",
    "    <td> [[-0.01057952 -0.00909008  0.00551454  0.02292208]]</td> \n",
    "  </tr>\n",
    "  \n",
    "\n",
    "  <tr>\n",
    "    <td>**b2**</td>\n",
    "    <td> [[ 0.]] </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 - The Loop ####\n",
    "\n",
    "**Question**: Implement `forward_propagation()`.\n",
    "\n",
    "**Instructions**:\n",
    "- Look above at the mathematical representation of your classifier.\n",
    "- You can use the function `sigmoid()`. It is built-in (imported) in the notebook.\n",
    "- You can use the function `np.tanh()`. It is part of the numpy library.\n",
    "- The steps you have to implement are:\n",
    "    1. Retrieve each parameter from the dictionary \"parameters\" (which is the output of `initialize_parameters()`) by using `parameters[\"..\"]`.\n",
    "    2. Implement Forward Propagation. Compute $Z^{[1]}, A^{[1]}, Z^{[2]}$ and $A^{[2]}$ (the vector of all your predictions on all the examples in the training set).\n",
    "- Values needed in the backpropagation are stored in \"`cache`\". The `cache` will be given as an input to the backpropagation function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: forward_propagation\n",
    "\n",
    "def forward_propagation(X, parameters):\n",
    "    \"\"\"\n",
    "    Argument:\n",
    "    X -- input data of size (n_x, m)\n",
    "    parameters -- python dictionary containing your parameters (output of initialization function)\n",
    "    \n",
    "    Returns:\n",
    "    A2 -- The sigmoid output of the second activation\n",
    "    cache -- a dictionary containing \"Z1\", \"A1\", \"Z2\" and \"A2\"\n",
    "    \"\"\"\n",
    "    # Retrieve each parameter from the dictionary \"parameters\"\n",
    "    ### START CODE HERE ### (≈ 4 lines of code)\n",
    "    W1 = parameters['W1']\n",
    "    b1 = parameters['b1']\n",
    "    W2 = parameters['W2']\n",
    "    b2 = parameters['b2']\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    # Implement Forward Propagation to calculate A2 (probabilities)\n",
    "    ### START CODE HERE ### (≈ 4 lines of code)\n",
    "    Z1 = np.dot(W1,X) + b1\n",
    "    A1 = np.tanh(Z1)\n",
    "    Z2 = np.dot(W2,A1) + b2\n",
    "    A2 = sigmoid(Z2)\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    assert(A2.shape == (1, X.shape[1]))\n",
    "    \n",
    "    cache = {\"Z1\": Z1,\n",
    "             \"A1\": A1,\n",
    "             \"Z2\": Z2,\n",
    "             \"A2\": A2}\n",
    "    \n",
    "    return A2, cache"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.262818640198 0.091999045227 -1.30766601287 0.212877681719\n"
     ]
    }
   ],
   "source": [
    "X_assess, parameters = forward_propagation_test_case()\n",
    "A2, cache = forward_propagation(X_assess, parameters)\n",
    "\n",
    "# Note: we use the mean here just to make sure that your output matches ours. \n",
    "print(np.mean(cache['Z1']) ,np.mean(cache['A1']),np.mean(cache['Z2']),np.mean(cache['A2']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "<table style=\"width:50%\">\n",
    "  <tr>\n",
    "    <td> 0.262818640198 0.091999045227 -1.30766601287 0.212877681719 </td> \n",
    "  </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that you have computed $A^{[2]}$ (in the Python variable \"`A2`\"), which contains $a^{[2](i)}$ for every example, you can compute the cost function as follows:\n",
    "\n",
    "$$J = - \\frac{1}{m} \\sum\\limits_{i = 1}^{m} \\large{(} \\small y^{(i)}\\log\\left(a^{[2] (i)}\\right) + (1-y^{(i)})\\log\\left(1- a^{[2] (i)}\\right) \\large{)} \\small\\tag{13}$$\n",
    "\n",
    "**Exercise**: Implement `compute_cost()` to compute the value of the cost $J$.\n",
    "\n",
    "**Instructions**:\n",
    "- There are many ways to implement the cross-entropy loss. To help you, we give you how we would have implemented\n",
    "$- \\sum\\limits_{i=0}^{m}  y^{(i)}\\log(a^{[2](i)})$:\n",
    "```python\n",
    "logprobs = np.multiply(np.log(A2),Y)\n",
    "cost = - np.sum(logprobs)                # no need to use a for loop!\n",
    "```\n",
    "\n",
    "(you can use either `np.multiply()` and then `np.sum()` or directly `np.dot()`).  \n",
    "Note that if you use `np.multiply` followed by `np.sum` the end result will be a type `float`, whereas if you use `np.dot`, the result will be a 2D numpy array.  We can use `np.squeeze()` to remove redundant dimensions (in the case of single float, this will be reduced to a zero-dimension array). We can cast the array as a type `float` using `float()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: compute_cost\n",
    "\n",
    "def compute_cost(A2, Y, parameters):\n",
    "    \"\"\"\n",
    "    Computes the cross-entropy cost given in equation (13)\n",
    "    \n",
    "    Arguments:\n",
    "    A2 -- The sigmoid output of the second activation, of shape (1, number of examples)\n",
    "    Y -- \"true\" labels vector of shape (1, number of examples)\n",
    "    parameters -- python dictionary containing your parameters W1, b1, W2 and b2\n",
    "    [Note that the parameters argument is not used in this function, \n",
    "    but the auto-grader currently expects this parameter.\n",
    "    Future version of this notebook will fix both the notebook \n",
    "    and the auto-grader so that `parameters` is not needed.\n",
    "    For now, please include `parameters` in the function signature,\n",
    "    and also when invoking this function.]\n",
    "    \n",
    "    Returns:\n",
    "    cost -- cross-entropy cost given equation (13)\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    m = Y.shape[1] # number of example\n",
    "\n",
    "    # Compute the cross-entropy cost\n",
    "    ### START CODE HERE ### (≈ 2 lines of code)\n",
    "    logprobs = np.multiply(Y,np.log(A2)) + np.multiply(1-Y,np.log(1-A2))\n",
    "    cost = - np.sum(logprobs)/m\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    cost = float(np.squeeze(cost))  # makes sure cost is the dimension we expect. \n",
    "                                    # E.g., turns [[17]] into 17 \n",
    "    assert(isinstance(cost, float))\n",
    "    \n",
    "    return cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cost = 0.6930587610394646\n"
     ]
    }
   ],
   "source": [
    "A2, Y_assess, parameters = compute_cost_test_case()\n",
    "\n",
    "print(\"cost = \" + str(compute_cost(A2, Y_assess, parameters)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "<table style=\"width:20%\">\n",
    "  <tr>\n",
    "    <td>**cost**</td>\n",
    "    <td> 0.693058761... </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the cache computed during forward propagation, you can now implement backward propagation.\n",
    "\n",
    "**Question**: Implement the function `backward_propagation()`.\n",
    "\n",
    "**Instructions**:\n",
    "Backpropagation is usually the hardest (most mathematical) part in deep learning. To help you, here again is the slide from the lecture on backpropagation. You'll want to use the six equations on the right of this slide, since you are building a vectorized implementation.  \n",
    "\n",
    "<img src=\"images/grad_summary.png\" style=\"width:600px;height:300px;\">\n",
    "\n",
    "<!--\n",
    "$\\frac{\\partial \\mathcal{J} }{ \\partial z_{2}^{(i)} } = \\frac{1}{m} (a^{[2](i)} - y^{(i)})$\n",
    "\n",
    "$\\frac{\\partial \\mathcal{J} }{ \\partial W_2 } = \\frac{\\partial \\mathcal{J} }{ \\partial z_{2}^{(i)} } a^{[1] (i) T} $\n",
    "\n",
    "$\\frac{\\partial \\mathcal{J} }{ \\partial b_2 } = \\sum_i{\\frac{\\partial \\mathcal{J} }{ \\partial z_{2}^{(i)}}}$\n",
    "\n",
    "$\\frac{\\partial \\mathcal{J} }{ \\partial z_{1}^{(i)} } =  W_2^T \\frac{\\partial \\mathcal{J} }{ \\partial z_{2}^{(i)} } * ( 1 - a^{[1] (i) 2}) $\n",
    "\n",
    "$\\frac{\\partial \\mathcal{J} }{ \\partial W_1 } = \\frac{\\partial \\mathcal{J} }{ \\partial z_{1}^{(i)} }  X^T $\n",
    "\n",
    "$\\frac{\\partial \\mathcal{J} _i }{ \\partial b_1 } = \\sum_i{\\frac{\\partial \\mathcal{J} }{ \\partial z_{1}^{(i)}}}$\n",
    "\n",
    "- Note that $*$ denotes elementwise multiplication.\n",
    "- The notation you will use is common in deep learning coding:\n",
    "    - dW1 = $\\frac{\\partial \\mathcal{J} }{ \\partial W_1 }$\n",
    "    - db1 = $\\frac{\\partial \\mathcal{J} }{ \\partial b_1 }$\n",
    "    - dW2 = $\\frac{\\partial \\mathcal{J} }{ \\partial W_2 }$\n",
    "    - db2 = $\\frac{\\partial \\mathcal{J} }{ \\partial b_2 }$\n",
    "    \n",
    "!-->\n",
    "\n",
    "- Tips:\n",
    "    - To compute dZ1 you'll need to compute $g^{[1]'}(Z^{[1]})$. Since $g^{[1]}(.)$ is the tanh activation function, if $a = g^{[1]}(z)$ then $g^{[1]'}(z) = 1-a^2$. So you can compute \n",
    "    $g^{[1]'}(Z^{[1]})$ using `(1 - np.power(A1, 2))`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: backward_propagation\n",
    "\n",
    "def backward_propagation(parameters, cache, X, Y):\n",
    "    \"\"\"\n",
    "    Implement the backward propagation using the instructions above.\n",
    "    \n",
    "    Arguments:\n",
    "    parameters -- python dictionary containing our parameters \n",
    "    cache -- a dictionary containing \"Z1\", \"A1\", \"Z2\" and \"A2\".\n",
    "    X -- input data of shape (2, number of examples)\n",
    "    Y -- \"true\" labels vector of shape (1, number of examples)\n",
    "    \n",
    "    Returns:\n",
    "    grads -- python dictionary containing your gradients with respect to different parameters\n",
    "    \"\"\"\n",
    "    m = X.shape[1]\n",
    "    \n",
    "    # First, retrieve W1 and W2 from the dictionary \"parameters\".\n",
    "    ### START CODE HERE ### (≈ 2 lines of code)\n",
    "    W1 = parameters['W1']\n",
    "    W2 = parameters['W2']\n",
    "    ### END CODE HERE ###\n",
    "        \n",
    "    # Retrieve also A1 and A2 from dictionary \"cache\".\n",
    "    ### START CODE HERE ### (≈ 2 lines of code)\n",
    "    A1 = cache['A1']\n",
    "    A2 = cache['A2']\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    # Backward propagation: calculate dW1, db1, dW2, db2. \n",
    "    ### START CODE HERE ### (≈ 6 lines of code, corresponding to 6 equations on slide above)\n",
    "    dZ2 = A2 - Y\n",
    "    dW2 = np.dot(dZ2, A1.T) / m\n",
    "    db2 = np.sum(dZ2, axis = 1, keepdims = True) / m\n",
    "    dZ1 = np.dot(W2.T, dZ2) * (1 - np.power(A1, 2))\n",
    "    dW1 = np.dot(dZ1, X.T) / m\n",
    "    db1 = np.sum(dZ1, axis = 1, keepdims = True) / m\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    grads = {\"dW1\": dW1,\n",
    "             \"db1\": db1,\n",
    "             \"dW2\": dW2,\n",
    "             \"db2\": db2}\n",
    "    \n",
    "    return grads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dW1 = [[ 0.00301023 -0.00747267]\n",
      " [ 0.00257968 -0.00641288]\n",
      " [-0.00156892  0.003893  ]\n",
      " [-0.00652037  0.01618243]]\n",
      "db1 = [[ 0.00176201]\n",
      " [ 0.00150995]\n",
      " [-0.00091736]\n",
      " [-0.00381422]]\n",
      "dW2 = [[ 0.00078841  0.01765429 -0.00084166 -0.01022527]]\n",
      "db2 = [[-0.16655712]]\n"
     ]
    }
   ],
   "source": [
    "parameters, cache, X_assess, Y_assess = backward_propagation_test_case()\n",
    "\n",
    "grads = backward_propagation(parameters, cache, X_assess, Y_assess)\n",
    "print (\"dW1 = \"+ str(grads[\"dW1\"]))\n",
    "print (\"db1 = \"+ str(grads[\"db1\"]))\n",
    "print (\"dW2 = \"+ str(grads[\"dW2\"]))\n",
    "print (\"db2 = \"+ str(grads[\"db2\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected output**:\n",
    "\n",
    "\n",
    "\n",
    "<table style=\"width:80%\">\n",
    "  <tr>\n",
    "    <td>**dW1**</td>\n",
    "    <td> [[ 0.00301023 -0.00747267]\n",
    " [ 0.00257968 -0.00641288]\n",
    " [-0.00156892  0.003893  ]\n",
    " [-0.00652037  0.01618243]] </td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**db1**</td>\n",
    "    <td>  [[ 0.00176201]\n",
    " [ 0.00150995]\n",
    " [-0.00091736]\n",
    " [-0.00381422]] </td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**dW2**</td>\n",
    "    <td> [[ 0.00078841  0.01765429 -0.00084166 -0.01022527]] </td> \n",
    "  </tr>\n",
    "  \n",
    "\n",
    "  <tr>\n",
    "    <td>**db2**</td>\n",
    "    <td> [[-0.16655712]] </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question**: Implement the update rule. Use gradient descent. You have to use (dW1, db1, dW2, db2) in order to update (W1, b1, W2, b2).\n",
    "\n",
    "**General gradient descent rule**: $ \\theta = \\theta - \\alpha \\frac{\\partial J }{ \\partial \\theta }$ where $\\alpha$ is the learning rate and $\\theta$ represents a parameter.\n",
    "\n",
    "**Illustration**: The gradient descent algorithm with a good learning rate (converging) and a bad learning rate (diverging). Images courtesy of Adam Harley.\n",
    "\n",
    "<img src=\"images/sgd.gif\" style=\"width:400;height:400;\"> <img src=\"images/sgd_bad.gif\" style=\"width:400;height:400;\">\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: update_parameters\n",
    "\n",
    "def update_parameters(parameters, grads, learning_rate = 1.2):\n",
    "    \"\"\"\n",
    "    Updates parameters using the gradient descent update rule given above\n",
    "    \n",
    "    Arguments:\n",
    "    parameters -- python dictionary containing your parameters \n",
    "    grads -- python dictionary containing your gradients \n",
    "    \n",
    "    Returns:\n",
    "    parameters -- python dictionary containing your updated parameters \n",
    "    \"\"\"\n",
    "    # Retrieve each parameter from the dictionary \"parameters\"\n",
    "    ### START CODE HERE ### (≈ 4 lines of code)\n",
    "    W1 = parameters['W1']\n",
    "    b1 = parameters['b1']\n",
    "    W2 = parameters['W2']\n",
    "    b2 = parameters['b2']\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    # Retrieve each gradient from the dictionary \"grads\"\n",
    "    ### START CODE HERE ### (≈ 4 lines of code)\n",
    "    dW1 = grads['dW1']\n",
    "    db1 = grads['db1']\n",
    "    dW2 = grads['dW2']\n",
    "    db2 = grads['db2']\n",
    "    ## END CODE HERE ###\n",
    "    \n",
    "    # Update rule for each parameter\n",
    "    ### START CODE HERE ### (≈ 4 lines of code)\n",
    "    W1 = W1 - learning_rate*dW1\n",
    "    b1 = b1 - learning_rate*db1\n",
    "    W2 = W2 - learning_rate*dW2\n",
    "    b2 = b2 - learning_rate*db2\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    parameters = {\"W1\": W1,\n",
    "                  \"b1\": b1,\n",
    "                  \"W2\": W2,\n",
    "                  \"b2\": b2}\n",
    "    \n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 = [[-0.00643025  0.01936718]\n",
      " [-0.02410458  0.03978052]\n",
      " [-0.01653973 -0.02096177]\n",
      " [ 0.01046864 -0.05990141]]\n",
      "b1 = [[ -1.02420756e-06]\n",
      " [  1.27373948e-05]\n",
      " [  8.32996807e-07]\n",
      " [ -3.20136836e-06]]\n",
      "W2 = [[-0.01041081 -0.04463285  0.01758031  0.04747113]]\n",
      "b2 = [[ 0.00010457]]\n"
     ]
    }
   ],
   "source": [
    "parameters, grads = update_parameters_test_case()\n",
    "parameters = update_parameters(parameters, grads)\n",
    "\n",
    "print(\"W1 = \" + str(parameters[\"W1\"]))\n",
    "print(\"b1 = \" + str(parameters[\"b1\"]))\n",
    "print(\"W2 = \" + str(parameters[\"W2\"]))\n",
    "print(\"b2 = \" + str(parameters[\"b2\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "\n",
    "<table style=\"width:80%\">\n",
    "  <tr>\n",
    "    <td>**W1**</td>\n",
    "    <td> [[-0.00643025  0.01936718]\n",
    " [-0.02410458  0.03978052]\n",
    " [-0.01653973 -0.02096177]\n",
    " [ 0.01046864 -0.05990141]]</td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**b1**</td>\n",
    "    <td> [[ -1.02420756e-06]\n",
    " [  1.27373948e-05]\n",
    " [  8.32996807e-07]\n",
    " [ -3.20136836e-06]]</td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**W2**</td>\n",
    "    <td> [[-0.01041081 -0.04463285  0.01758031  0.04747113]] </td> \n",
    "  </tr>\n",
    "  \n",
    "\n",
    "  <tr>\n",
    "    <td>**b2**</td>\n",
    "    <td> [[ 0.00010457]] </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.4 - Integrate parts 4.1, 4.2 and 4.3 in nn_model() ####\n",
    "\n",
    "**Question**: Build your neural network model in `nn_model()`.\n",
    "\n",
    "**Instructions**: The neural network model has to use the previous functions in the right order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: nn_model\n",
    "\n",
    "def nn_model(X, Y, n_h, num_iterations = 10000, print_cost=False):\n",
    "    \"\"\"\n",
    "    Arguments:\n",
    "    X -- dataset of shape (2, number of examples)\n",
    "    Y -- labels of shape (1, number of examples)\n",
    "    n_h -- size of the hidden layer\n",
    "    num_iterations -- Number of iterations in gradient descent loop\n",
    "    print_cost -- if True, print the cost every 1000 iterations\n",
    "    \n",
    "    Returns:\n",
    "    parameters -- parameters learnt by the model. They can then be used to predict.\n",
    "    \"\"\"\n",
    "    \n",
    "    np.random.seed(3)\n",
    "    n_x = layer_sizes(X, Y)[0]\n",
    "    n_y = layer_sizes(X, Y)[2]\n",
    "    \n",
    "    # Initialize parameters\n",
    "    ### START CODE HERE ### (≈ 1 line of code)\n",
    "    parameters = initialize_parameters(n_x, n_h, n_y)\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    # Loop (gradient descent)\n",
    "\n",
    "    for i in range(0, num_iterations):\n",
    "         \n",
    "        ### START CODE HERE ### (≈ 4 lines of code)\n",
    "        # Forward propagation. Inputs: \"X, parameters\". Outputs: \"A2, cache\".\n",
    "        A2, cache = forward_propagation(X, parameters)\n",
    "        \n",
    "        # Cost function. Inputs: \"A2, Y, parameters\". Outputs: \"cost\".\n",
    "        cost = compute_cost(A2, Y, parameters)\n",
    " \n",
    "        # Backpropagation. Inputs: \"parameters, cache, X, Y\". Outputs: \"grads\".\n",
    "        grads = backward_propagation(parameters, cache, X, Y)\n",
    " \n",
    "        # Gradient descent parameter update. Inputs: \"parameters, grads\". Outputs: \"parameters\".\n",
    "        parameters = update_parameters(parameters, grads)\n",
    "        \n",
    "        ### END CODE HERE ###\n",
    "        \n",
    "        # Print the cost every 1000 iterations\n",
    "        if print_cost and i % 1000 == 0:\n",
    "            print (\"Cost after iteration %i: %f\" %(i, cost))\n",
    "\n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost after iteration 0: 0.692739\n",
      "Cost after iteration 1000: 0.000218\n",
      "Cost after iteration 2000: 0.000107\n",
      "Cost after iteration 3000: 0.000071\n",
      "Cost after iteration 4000: 0.000053\n",
      "Cost after iteration 5000: 0.000042\n",
      "Cost after iteration 6000: 0.000035\n",
      "Cost after iteration 7000: 0.000030\n",
      "Cost after iteration 8000: 0.000026\n",
      "Cost after iteration 9000: 0.000023\n",
      "W1 = [[-0.65848169  1.21866811]\n",
      " [-0.76204273  1.39377573]\n",
      " [ 0.5792005  -1.10397703]\n",
      " [ 0.76773391 -1.41477129]]\n",
      "b1 = [[ 0.287592  ]\n",
      " [ 0.3511264 ]\n",
      " [-0.2431246 ]\n",
      " [-0.35772805]]\n",
      "W2 = [[-2.45566237 -3.27042274  2.00784958  3.36773273]]\n",
      "b2 = [[ 0.20459656]]\n"
     ]
    }
   ],
   "source": [
    "X_assess, Y_assess = nn_model_test_case()\n",
    "parameters = nn_model(X_assess, Y_assess, 4, num_iterations=10000, print_cost=True)\n",
    "print(\"W1 = \" + str(parameters[\"W1\"]))\n",
    "print(\"b1 = \" + str(parameters[\"b1\"]))\n",
    "print(\"W2 = \" + str(parameters[\"W2\"]))\n",
    "print(\"b2 = \" + str(parameters[\"b2\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table style=\"width:90%\">\n",
    "\n",
    "<tr> \n",
    "    <td> \n",
    "        **cost after iteration 0**\n",
    "    </td>\n",
    "    <td> \n",
    "        0.692739\n",
    "    </td>\n",
    "</tr>\n",
    "\n",
    "<tr> \n",
    "    <td> \n",
    "        <center> $\\vdots$ </center>\n",
    "    </td>\n",
    "    <td> \n",
    "        <center> $\\vdots$ </center>\n",
    "    </td>\n",
    "</tr>\n",
    "\n",
    "  <tr>\n",
    "    <td>**W1**</td>\n",
    "    <td> [[-0.65848169  1.21866811]\n",
    " [-0.76204273  1.39377573]\n",
    " [ 0.5792005  -1.10397703]\n",
    " [ 0.76773391 -1.41477129]]</td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**b1**</td>\n",
    "    <td> [[ 0.287592  ]\n",
    " [ 0.3511264 ]\n",
    " [-0.2431246 ]\n",
    " [-0.35772805]] </td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**W2**</td>\n",
    "    <td> [[-2.45566237 -3.27042274  2.00784958  3.36773273]] </td> \n",
    "  </tr>\n",
    "  \n",
    "\n",
    "  <tr>\n",
    "    <td>**b2**</td>\n",
    "    <td> [[ 0.20459656]] </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.5 Predictions\n",
    "\n",
    "**Question**: Use your model to predict by building predict().\n",
    "Use forward propagation to predict results.\n",
    "\n",
    "**Reminder**: predictions = $y_{prediction} = \\mathbb 1 \\text{{activation > 0.5}} = \\begin{cases}\n",
    "      1 & \\text{if}\\ activation > 0.5 \\\\\n",
    "      0 & \\text{otherwise}\n",
    "    \\end{cases}$  \n",
    "    \n",
    "As an example, if you would like to set the entries of a matrix X to 0 and 1 based on a threshold you would do: ```X_new = (X > threshold)```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: predict\n",
    "\n",
    "def predict(parameters, X):\n",
    "    \"\"\"\n",
    "    Using the learned parameters, predicts a class for each example in X\n",
    "    \n",
    "    Arguments:\n",
    "    parameters -- python dictionary containing your parameters \n",
    "    X -- input data of size (n_x, m)\n",
    "    \n",
    "    Returns\n",
    "    predictions -- vector of predictions of our model (red: 0 / blue: 1)\n",
    "    \"\"\"\n",
    "    \n",
    "    # Computes probabilities using forward propagation, and classifies to 0/1 using 0.5 as the threshold.\n",
    "    ### START CODE HERE ### (≈ 2 lines of code)\n",
    "    A2, cache = forward_propagation(X, parameters)\n",
    "    predictions = (sigmoid(cache['Z2']) > 0.5)\n",
    "    ### END CODE HERE ###\n",
    "    '''Another way of doing prediction:\n",
    "    predictions = list(map(lambda x : 0 if x < 0.5 else 1, A2[0]))\n",
    "    predictions = np.array([predictions])'''\n",
    "    \n",
    "    return predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predictions mean = 0.666666666667\n"
     ]
    }
   ],
   "source": [
    "parameters, X_assess = predict_test_case()\n",
    "\n",
    "predictions = predict(parameters, X_assess)\n",
    "print(\"predictions mean = \" + str(np.mean(predictions)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**: \n",
    "\n",
    "\n",
    "<table style=\"width:40%\">\n",
    "  <tr>\n",
    "    <td>**predictions mean**</td>\n",
    "    <td> 0.666666666667 </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is time to run the model and see how it performs on a planar dataset. Run the following code to test your model with a single hidden layer of $n_h$ hidden units."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost after iteration 0: 0.693048\n",
      "Cost after iteration 1000: 0.288083\n",
      "Cost after iteration 2000: 0.254385\n",
      "Cost after iteration 3000: 0.233864\n",
      "Cost after iteration 4000: 0.226792\n",
      "Cost after iteration 5000: 0.222644\n",
      "Cost after iteration 6000: 0.219731\n",
      "Cost after iteration 7000: 0.217504\n",
      "Cost after iteration 8000: 0.219472\n",
      "Cost after iteration 9000: 0.218562\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f1764027ac8>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QAhrstGAQEQwDatXu222nP3VyRIQ7i+eYGzjQuc11marMQ3zj51NKceVClUpl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\nGx6kETlYgpnR8JAi1mH68g2TYmaSzZl5JpeWBy4IdtVFhBjQNQmJXO1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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1764597208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build a model with a n_h-dimensional hidden layer\n",
    "parameters = nn_model(X, Y, n_h = 4, num_iterations = 10000, print_cost=True)\n",
    "\n",
    "# Plot the decision boundary\n",
    "plot_decision_boundary(lambda x: predict(parameters, x.T), X, Y)\n",
    "plt.title(\"Decision Boundary for hidden layer size \" + str(4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table style=\"width:40%\">\n",
    "  <tr>\n",
    "    <td>**Cost after iteration 9000**</td>\n",
    "    <td> 0.218607 </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 90%\n"
     ]
    }
   ],
   "source": [
    "# Print accuracy\n",
    "predictions = predict(parameters, X)\n",
    "print ('Accuracy: %d' % float((np.dot(Y,predictions.T) + np.dot(1-Y,1-predictions.T))/float(Y.size)*100) + '%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**: \n",
    "\n",
    "<table style=\"width:15%\">\n",
    "  <tr>\n",
    "    <td>**Accuracy**</td>\n",
    "    <td> 90% </td> \n",
    "  </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accuracy is really high compared to Logistic Regression. The model has learnt the leaf patterns of the flower! Neural networks are able to learn even highly non-linear decision boundaries, unlike logistic regression. \n",
    "\n",
    "Now, let's try out several hidden layer sizes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.6 - Tuning hidden layer size (optional/ungraded exercise) ###\n",
    "\n",
    "Run the following code. It may take 1-2 minutes. You will observe different behaviors of the model for various hidden layer sizes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for 1 hidden units: 67.5 %\n",
      "Accuracy for 2 hidden units: 67.25 %\n",
      "Accuracy for 3 hidden units: 90.75 %\n",
      "Accuracy for 4 hidden units: 90.5 %\n",
      "Accuracy for 5 hidden units: 91.25 %\n",
      "Accuracy for 20 hidden units: 90.5 %\n",
      "Accuracy for 50 hidden units: 90.75 %\n"
     ]
    },
    {
     "data": {
      "image/png": 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sL3jN5S+GAecuxshvBqyvdu7EhuF8Oi/GQRAm0EmNmtaqet+zCS0LJi44+J5G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\nIoUascUK9jpujhRqdTdXPeySw2JnmHzX3nXzzo8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aipPZq1xIPtDcxGgpP6XNH2xsVSMj\nBUyEepiz08TdAkPFyZbUn51O0i2SdIsbbieAh/M3eTh/E4B30yc5n36k5ff0tdmNRxmVNZtb0QEE\nisHiBGFZ29axfxlYlsaRB0K8F/MPxjQkZT1ExKvS229iGIKFjIv0wA4Jevst7NDuO6EtBbHxuRKp\n+XrDkKUuffVvgCI9W8KqeivzXjXB9GACu+Quj98pxa09X+saEBCwfXQUj2SvcSl5bEtujq/ymALG\nwz3MW2kSToHB0vY76n9ZJJ0CSaew4XYCeCR/g0fyNwB4p+MMn6YebPk9fd0n22otZd3mVqTu5qHi\nBKHd5Ga77uZ3Y/5t2nUkFd0mLGv0DZiYpmBh3kVKCIUFPX0Wtr373LzEudQjfJx+BIHEFQZKCAxP\ngadIzzTc3Jj3qjTB9FCCUMnFCtwcsA2CQHaH8GTmIjejB8iaqwYTCwFKgmykUKySgSFdzmYuAvX2\n5X8/8AIZK4lEQ0diSYc/GP/Vnu3au5qnFy4S8aqc6zhBTbOIuiW+NvshQ+WpdV/3RWyIN7ufRDQC\nftX1BF+f/ZAHCrfux2HfFYQQHKzO8Lkda+n8rICkk1/erqvHpKtn97ZYX11XY5ccUvM+LegbaAqi\nuSrZrvBK6rAQVKMm1eju/R0EBATcX57OfMpIdICcGW91s2r06F7r5oVLQH1G6N8NvMiiFcdbcrNX\n4w/Hf7Vnu/au5pnMecJumY87TlDTTGINNw9u4ObP44d5q+uJuptVfazPCzPvc6y4eyY2CCE4UJ3l\nihVtncqgIOEUl7fb7W5ezXi4p1FXrUOj8dfa2aXRbJVsZ7ObK1GTSuDmgG0QBLI7BA1F0Yi2rvAK\njXqLtsbjSmEol+dmP1qeEXcufYJ5K7W8YizRcYXOaz1P74ka2o0QwOncVU7nrm76NQU9zJvdZ5tO\ntgC/6T7LQHlmV11kPLZ4meuxQRzNWBamIV2emv8UQ+2uu/LtWNscIrZYaTsrbRlRn11aXmdUTkB7\nhCeJ5mvojqQaNuoXGUE6V8A+Q0NRaOdm1exmU7n1QK0xW/2DjpNkrOTy5IElN7/e89SeqKHdCAE8\nmrvCo7nNd9/NGVHe6nq8xc2v9TzFwO1ZIt7uGUnz+MJn3IwebHHz0/Pnd13G3Gb5LHEUV2zgXAFW\nNXDzdgnc3EwQyO4g2l6Xr/6CCoFUguHS+PJDV33mqiqhMR3q2rNde++UG7FD+I2HlkLnbw98gz8c\n/yXRbQrT8xTlkkTTIBzREPf4BBN3S3x/7OecSz/CRLiXqFvm0cXLDDUupnY7fh0ONW8TrULU7p+P\n9mVhVlz6budAqeWa4ppdb4AVpHwF7DvafeXXuNlTYjmIBbgaH2qZq6qExkS4Z8927b1TbsQO+TaC\nkkLnbxpujmxz9vmym3UIh++9mxNuseHmE0yEe4i6JR5bvMxgaf070ruZqmZtHFSp9ee9BrTHqrj0\nrnVzyGD6UGLfTgYIAtkdxOHiGDdih5AbrGbpSnI1OshsqIOCEaW2TofAvdq1907xhIb0O9kKQcGI\n8JP+r/P9sV8gqA96d4VBxCtvGDwtzDvMTrvL53FNg4ND9j2vRY27JV6Y/ZBiWXHFPsjFziFGQv2c\nKNxYt3HWTma9Fv2lhEW42H4w+lLXw5odrPiuRUhFNFshVHJxTY18OtTcuVkpuifyiFWzYIWqr6DH\nM+VdP1A+IGCrDBfHuRk92JoiugZDSa7FDjFjd1EwIjhivUus/XnRuRGe0OrXLWsRgrwR4ad9X+d7\n468CW3NzZs5hbqbuZgXoGhwctu95LWrcLfH8zPsUK4IroYNc6BhmJDTAicJ1OmvZe/re95tHf9fl\nf8weoWO6tL6bbR0nFIQfaxFSEV2sECq7OKZGwcfNXeN5NLlyUS9UPbiNL5TJd+5PNwffpB3EV+Y+\nYSbUSVkP4QgDgaqvTK45qXtC492ux5CNuWTLtTqrt1OSzurCnu7aeycMFSc4lz6B53dhIgQ5M86k\n3c3HHQ8zEe5BKEXYq/LCzPscqMz47rNcksxOuyi10gtEShi7VeXIA6F7uvqrlGJiwuONky9RSHTU\nOyVKydXkYb4699FyY6zdwKO/6/Id7b9dd5tiwiaeqWBVvRZhKurz0WYPxPd1uo0fmifpu7mI7ioa\nRQvEFyrMHEosD1TXXYnutE6E1BTEstUgkA3Yd3x17iNm7A6qur2umx2h807n4xu6uacyjxlkSvky\nXJzg49TDbdyssWglmAh18VH6BJPhboSCiFfmhZn3Gaj4z10tlTzmZprd7EoYG7k/bh6b8Hjz1MsU\n46llN19JHua52XM8VBi5Z+99P1leeE4q4gtVzJq/mythg7mDG3Qk3odorqT/5iKat+LmxBo3G45E\nd1tT0uturu3bQDa4t7+DCMsqf3L7p7ww8z5nFy5yNnMRfW3qkZJIoeNp+srqcOP/tca2RmPszIsz\n793Pw99VdDg5TmWv1C80fBBK8nrPk4yHexq/b4OCGeVn/V8ja/h3CV7MuE2D0JfwZD3IvZcU8pJr\n8aGVIBZA0/A0g992PU5t3TsDO4cfvvKDDYNYAIRgajhJOWrW56E1/icF5NI200NJ3xl0+53UdBGj\nEcRC/Z6QpqBrPI/vl3ctwbpAwD4k4lX5s1VufiJzqdXNsj5ndSM3h2SNF2ffv5+Hv6vorC1yIntt\nXTe/1v00E8tu1smbMX7a/3Xyhv+F/OK819bNlfI9dnNOci05vBLEwoqbu5/A2aiedBfQlD2lCSYP\nJylHfNzcEWJmKLnnxr/cDdLTRXSv1c2dE5t08z5md1zd7iN0FEeKY9Do3h/xKvy263FEYxh5xC1R\nMsK4Yk13NyEIOVWOF0ZIOkWOFm4HtbEb8HTmAo4w+Cx5rCVlzNN0SiKEWiMZTwje7zhFzoqRsVLY\nXpVHFy5zKncVz/M/2SgJE2M1Ekmdjq76GJy7TW7RZeaR4ZaZdfVj1vn3Q9/lwfxNzmYu3rPvhZSK\n+VmHxQUPJSES1ejpN7GsjaXlNyh9Q4Rg9lACq+wQzdVHMxQTFrVw0PnQF6WI5Wq+sajmKXRH4lk6\nnqnjmnrLiroU9eH1AQH7ER3Z5OawV+GdrseW3Rx1SxTbuDnsVDhWuEXSKXKscDu4G7sBz2bO42gG\nnyeO+Lq5LOyWx6UQvNtxmqyVIGMlCTfcfDJ3FU+2d/P4aN3NnV0m+j1wc3bRZebUYV83u0Ln3w39\nPg/mb/Bk5tI9+15IqZibccgubt3NG+FbAiQEs4Nr3WxTCwchhy9KEc37u1l3FborG17W8EwNUZOB\nm1cRfKt2OA/lb3KscJs5O43t1TClw48GX/HdNu4WeSZz4T4f4e7myYWL3IoeoKyHlptyGNJlsDjO\naKSfta04lNC5GVuplSobYd7tPMOl5DEGoqOkLl4mVGqdt+e5sDDvkc96DB8Loet3X5i6U2tNYwMQ\ngppucSlxjIlwD99r1P7ebSZGa5SKcnnxsFiQ3LpR5fCx0LrB+w9f+QH85fbftxY2g+B1E5jV9o1l\nBDQ1cZo7EKP3Vg6xpqFELh2+D0caELDzeSR/g+OFW8zbaWyviq4kf3Ho277bJpxC4OYt8lTmArej\nA5Q1G7nWzdGBFjdLoXMzdmjZzSUjzDsNN/eFb9Px2WXsUuuces+FhYxHPucxfPTeuNnYwM2fddmL\nGAAAIABJREFUJY4zFermPxv/5T1x8/jtGuVSq5uPHAttO3hfr4fFEoGbN4e1WTcLwexAnN7bzW6u\nhg3yHaH7c7A7kCCQ3QUYyqOvMrf8c1dtgRm7o2lF0pAOp7Obb3EfUMeWDt8b+wWfph5gJHqQkFfl\n1OIVumoL3IoebH2Bki0dFZWmk7MSFHoeQrzwAKfffZVkxr+O1vPqKcid3Xf35J5IGRy4fYX5vkO+\nK78AUtPJmTHGwn0c2mCO31apVmVTELuEkpDNuHT6zMjbTC1swN1DKIUS+I4tUoKmVGzHNhg/liaS\nr6G7kmrIoBoxgprjgIBVmGvc3FHLMWelUNpqN7ucym5+NFxAnZCs8b3Rn/Np6iFuRQcIuRVOZ6+Q\nrmW35OaslSDf9zCi50HOvPNzEgtzPq+tB7TZBZeOrrvr5mTK4MCtL8j0HGjrZk/T67W/4R4OlP2v\nHbZLtSKbgtgllITFha1fiwTevvsI2d7NUqMpFdsJrXFz2KAa3t9uDhLVdyHfnPot6VoWQ7qYXg1d\nepxavMrh4tiXfWi7kpCs8VTmIn8y+jN+f+I1DpfGibsljhVuYciVVB+hGqmWbU4YUtPxDJMrZ5/D\nDPlvoxQUi3e/JicW1zhYm2Hw6qdonluPmH1whc68nbrr71+rKN/6SaWg7FODtOla2IDtoeqpwquv\nXmohw7cbqKJeu9TyuCYoJm1ynWGq+3xOXUDAZvjW1FuknByGdLAabj69+DmHV43LC9g8YVnj6cyn\ndTdPvs5waYKkW+RwcbTJzdom3Xz17HNY67m5cA/cnNA4VJ7m0LWLiHXc7KExb919N1er0vfXotTW\n64MDb98FZKubqyHD//oJyHW0ZkE1uTkSuDm4I7sLiXoVvj/2C+atFCU9RHd1gbDc3ly1gPY8P/sB\nXdUFLiaP42gGQ8UJFs0Ek+HudU8cRTvO69/4Rxy8cpHBq5+2nJ9M8+6fdIQQuI5i+OoFBm5fZeT4\nGaaGjiP15j9xQ3rEndbU5zvFtNuMeRJgr7lw2ExKUsDm0B0P3VU4tl5PP1KK5FyZRKZc30DAYmeY\nfEcYhGC+P0bXRB7RWHeQoj6mKLdPux0GBNxNol6ZPx77+So3ZwjL2pd9WHuOF2fe51Iiw6XkMRzN\nYLg4zryVYjrcve7r8qEkr33jH3HwygUGr15odbN1bwICx1UcvnKegVtXGHnwUaYOHUWtcbOOJO60\npj7fKZal+fYKEoItjQUMvL01NnazaLg5BJpgri9G12Sh2c227hvIBjQTBLK7FAG7dj7obkEAJ3PX\nOJm7tvzYlN3JPwy8gLteF2AhcHSLW8dP4ek6Rz7/uOlpw4TrV8pID6yQoLffJBS6s86F1YqkWqnb\nyqpWOHr5Q2YPDCOFVh9mS73bo6kchosTd/RefoRCGqGwoFJWTdLUBKQ66qlLgQjvHsJTdE3kCZWc\nRhENZDvrwktkymhL/wYKUnNllCYopMOU4xaTh1PEFivorqQcsyjFNzHAPiAgYFMEbr73aChO5a5y\nKreSsj0Z6uIn/c/japtx82mk0Dh85XzT04ax4ma74Wb7jt2sqFXrJ2S7WubopQ+YHRjCXe1mKbG8\nGoOlu+9mOySwQ4JKRTUtNgsBqfTGIUCQSrw1hCfpHi9gl1fcvNgVQVNqjZsVqbkSUhcUUyHKCZvJ\nkBG4eRsEgWwAClg04wgUSacQTNhYh77qPC9PvcXbXY+xaCbqD7ZLZzJMxo88wuGr59FUPb3HtiEz\nu5JaVCkpbl2vcWjYIhLdvjAdR9UHvTdOkrrn8dhbP+GLR58jl+4GAf3lWV6YfR+dezNu4OCgzfSk\nwy27l2uPnKUUSxJ1y8iFi/yb5/0blAVsj87JehCrLc02AJJzZRCsiLKBpiA5X6HQaNTkWjqLPdH7\ne8ABAQFbpu7mBBqSRODmdemvzPHNqd/ydtdjZM3GnNJ13Dx27CTD1z5FUwqhgW0L5le5uVxSjFyv\nMXjYIhy5e242PJfH3/wJnz/2VfKpupsHKjO8MPM+um9a050hhODgkM3MpMPNUB/XHz5LOZYg6pZg\n4SLHC7fbvjZYfN46XZMF7JJTr9ts/HOmZkvruLlMMVUv7QncvD2CQHafM2V38su+r1DVLAAibpmX\np39LZy0LQFmz+DxxhHkrRXd1gYfyN7Cl82Ue8pfOofI0fzr6MzwEr/U8zUj0AJ7QfaUpNEHfg0nC\n1RJCg5tX/VPAx27VOPrg9jsm2qHW9KFIMc/jb/+URI9NZ7eJuXbu4TZRSuHUFLoumjoearrAO36I\ni33P4TVWxQtWjF/3PUNsobwcSAXcGZoniRSdlsYQGu3HzfkNUQ8ICNi5TIa6+GXvs9Q0ExBE3RLf\nmvotaScHQFm3uRw/QsZK0l3N8FD+5r5382B5isHRn+Ih+HXvM9yKHMATmn9AqwkGHkxg1yogFCPX\n/FPAR0fuzM2hkPBxc44nfvtTkr0hOrqMe+5mXRc4xwf5rO+rq9wc543uJ3GFwcP5Gy37CoLYraO5\nknDRaVlwCtx8bwmaPe1jyprFTwaep2hEcDUDVzPImTH+buBFXKGzYMb50eArnEuf4Hp8iA86TvKj\nwVfIGcGKEdRn/r408y5/PPoz0o3Afy0CRVzUsEMa5VL7E5ZScPtmFbXNwdemKUgk9RZfaxp0pcRd\nE2Vu0eXaFxVGrle5fqXC2K1q0/zc9ztOL4ty+Rga6a3BUO+7g1V2/euR16Fm31l6XEBAwP2jpNv8\npP/rlIwIrmbiagZZM8aPD7yIh0bGTPCjQ9/ho/QjXI8P8WHHKX506DvkjaDWHepu/ub0O/zx2M9I\nNQL/lm2UJKY52CGNSqn9CVUpGB25AzdbGvFEOzdz19ycXXS59vmKm8dvV5Gr3dzZ6mZXM3i/41SL\nToIgdntYpa272QncfMcEgew+5lp8CLl27UgIXKEzEj3Am91nqWnG8snP0wyqmslvux77Eo5255J0\ni3x99kN02TzM3JAujy1cWk7l3WhFt1ZVTI7VcJ3tCbN3wKS718C0BLoOiZTO0NH1Z7huhVLJY3Lc\nQXp1uS91YJ4YXVnJXrTivq/VPIVoM5Q+YPNYZZfu8bzvcwqohHXkmn9uKWAhSFcKCNg1XI0Nt4yS\nQWg4wuBWdIA31rjZ1QwqusXbnY9+CUe7c0k6Bb4++2FTh2Oou/mJzCW0RtShbeDmakUxOe5s2819\nB0y6ltxsQDKl39WZtaWix9S4g5Sr3FyQTIytuHk53XoNVd3CFfVg6oev/CAIYreJVXbonlzPzUbg\n5ntEkFq8jynq4ZYVOgBP6FyLHmIq1NqdVwmN0XDfpt9j6bS/12t7+qrzfGfyDd7tfJR5K0nEq/D4\nwiUeyt9c3iYa1ZpqZfzI5yTFQoXBw/aWOgpCvRYm3WmS7rw3A8inJ3xSrxSUSxKnJjEtjWI45Dvc\nW2piZah3wLZJzxRb6myg/ncmNcF8fxzTkSRnS5iOh2PpLHZH6i36AwICdgVFY303z4Q6Qazxg9AY\njfRv+j32i5v7K3N8e/JN3u06w4KZJOKVeXzhEg/mR5a3ica05cY87chnPYp5b9tu7ug06biPblYK\nSkWJ6ygMUxBziyxayZbtLOlgKC8IYO+QjukN3DwQw6x5JGfLmI5Hza67uRYO3HynBIHsPqa/Mst5\n9aCPEAXj4V6UkiC2l/ZQ0m3e6nqCW9EDKGCoOMFzc+eIepU7P/AdykBllj8af7Xt80ITHDpscfvG\n+uMYpKyLafBI62zPL4t8zqPWZsKTEKD9by/ww9dOEs7X6JrIN53QpYBsZyjovncXsCpu2+cmhxN4\nlo5n6VSirRcsAQEBu4OB8gwXksd93TwW6UO1ibha7uL6UNRDDTcPIICh4jjPzZ0j4u3dEX4HKjN8\nb6y9mzVNMDhscfvmJtw86TB42L7bh7ht8lmPWpvDFgJctx7IPpW5wK97nmnq6mxIl8czn/HfB0Hs\nHWNV2qeITw4n8Ewdz9SpRK37eFT7gyC1eB9zqDTVVntKaKRrOf/bh0KQNWNt9+sh+JsDLzESPYAU\nGkpo3IoO8DcHXsLb51+5cFjn2EP2Utf9tpTLats1OfeC+dn2TURqQud/+NkDAJTjFvP9MVyjnrTl\n6YLF7kh9juk+Q3MlZsWFu5hSLXX/L44S4JlBrU1AwF5gsDTZ1s2eppOsFXzdrIRYt4eFh8bfHHiJ\nW9EBlNCQQmMkeqDh5v290BiO6Bx9cBNuLskd5ea5ddwsJVh2/d/1cHGcr8++T9QpglKEvArGSyY/\neu4b9+tQdwz6PXGz/9+P0gI332uCO7L7GA1FR3WR+VCHz3OSiFdhwecumiE95q0kSafgu99b0QEq\nuo1atZqshEZVt7gZPcCx4ujd+xC7EF3XGD5aH1VTLPg3gNppNy+dWrs7APDZE4/j2iurjKWETSlh\nr1xo7bQPc48RnqR7okCo5KAa6WoLPZG70rU51xEiNVtqueOdTwd3vAMC9goailQtx4KdannOkB4R\nr0JWtGZdGNIlYyVJuEXf/d6MHqCqWy1urug2t6IDHCmO370PsQsxDI2hhptL67hZ7KBz7Xp1ux1d\nOtqqkp7jhVGOF0bxEPzz7/w3MCH2fm75KjRP0jVeIFRuuBnI9ESXx9/cCdmOMKm5Vjfn0uHAzfeY\nIJDd5zy1cIFXe7/akm5yZuFzXM1gMtyD1JpXk5QQbYNYqM+9c3xSkh1hsGgloAh5I8K7HWcYi/Rh\nSoeT2auczl5Zbr6w1zEtjYNDNtMTVRYXmoUpBI0OxNs/+bmOYn62HijrBnR0mcQTm1sVVEqRz3os\nLriAIJnSsUOCsk9nR9c0+eTrz/nvaJ+evLsnCtiNFvxLI3LSMyVc687TivLpELoriS9UQNT3X0za\nLHYH3UoDAvYST2Uu8KveZ1vc/OjiZSqazXS4C7nGs1Jo67vZSuCI1ss+R+iNuejj5Iwo73WeYSzc\niyUdTmavcCp7dd+42bI0Dg3ZTE1Uyfq5OXVnd9echptL23RzLuuSXfBYcrNlCyrl1n8bTYOuHv/6\ny3/+yj+9k4+wa+kaz2OX3CY3d0wXcU2davTOalXzHQ03L1aW662LSZts1/7LRrvfBIHsPmewNMUL\nM+/xbuejFIwItqzx6MJlzmS/oKiHuZQ8hmTlJKtLj67qwvKcWT/StRym8nDW1PeY0iVdy1LWbf7y\n4MtUNROERk23+LDjJAt2khdn3r9nn3Un0t1nUXNqlIty+eQXCmv09K+cVGs1SakgERrE4vqGnQ5d\nVzFyvYLXKNlwHJgcq1HtNujqXv9kLT3FzWsV3OVSTEW5JLFDoqVRlWsYfPA7L6A2ysXaR+iuJFTy\nmSOnIDFfufP6GCFY7ImS7YpgOF49hbtNunFAQMDuZbg0wfMz7/Nu5xmKDTc/tvAZp7NXKBgRPkse\nbQpkNenRW51fnjPrR7qWxZQujt7sAVN5pGs5inqIvzr4MjXNQC27+RQLZoIX5j68Z591J9LTZ+HU\navWxeQ03hyMaPX2tbtYabt6o+7HrKG75uLnWbdC5gZs9TzGySTcLAT19Zsti+H/8l/+Y8z9uvcu/\nH9AdD7vsthS3aQoSmTKzdxjIIgSLvQ03u4Gb7ydBIBvA0eIYR4tjeGhoqwbyxLwy3x1/jTd6zjJv\npRAoDhdH+drsuXX3N1SaIOxV8IS2LFqhJCFZZbg4wUfph+vt3lcFup5mcD06yFnjInG3dK8+6o5D\n0wSHhmyqVUmtorBs0dQRcXbaYWG+YS4B0xMOBwYtorH2K7gLcw7emqwopSAz65LuMNoGwkopblxd\nkexqqlXFwL94gnP/aoaOmRmK8Tjnv/oVbj9wfMufeS9hVlxSsyWsiotr6RQTFgr/bC3dvTvzAgGU\nJnDs4PQdELCXOVYc5VhxtMXNcbfEdyde543us2SsJEIpjhVv89zsR+vub7g4wXudVVxNX04v1pRH\n2KswVJrgg46TOEJvSj12NYNr8WGeXLi4p5s1rkXTBIeGbaoVSa3q4+apGguZxjldAJMOBwctItH2\nbs7MOy1+VQrmG25uFwhLKblxtYps4+buXoNCTlKtSExT0NnTepf3h6/8AH68qY+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YAAAg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\nYPkHqVSG9oj7Dk8Xv92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TcBxNteJhmtHbu7RaDreE7wcpSrF4+FjjjctXr9zEN8zgLKGB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3/0I7jrxvcRyeYwf+sWFMOo7R9wJIrs2Bh2Lsz39X31sAJblaAYjmdmDQNQSETw1MrjePuX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3LDQarBOeBgDlkovqlPSKFWFMAoFgUkwq1FiPUPisxwCgNZeHz816hCKVkQBCcLR8G5B6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IdjK1O9lac7tzeWXx0pu+FX78qYGzYXz/5FerplIW09kWT3z242Qr63EE2SeSjpRNyz75JkhR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\nogIYZyEneQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f17640117f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This may take about 2 minutes to run\n",
    "\n",
    "plt.figure(figsize=(16, 32))\n",
    "hidden_layer_sizes = [1, 2, 3, 4, 5, 20, 50]\n",
    "for i, n_h in enumerate(hidden_layer_sizes):\n",
    "    plt.subplot(5, 2, i+1)\n",
    "    plt.title('Hidden Layer of size %d' % n_h)\n",
    "    parameters = nn_model(X, Y, n_h, num_iterations = 5000)\n",
    "    plot_decision_boundary(lambda x: predict(parameters, x.T), X, Y)\n",
    "    predictions = predict(parameters, X)\n",
    "    accuracy = float((np.dot(Y,predictions.T) + np.dot(1-Y,1-predictions.T))/float(Y.size)*100)\n",
    "    print (\"Accuracy for {} hidden units: {} %\".format(n_h, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Interpretation**:\n",
    "- The larger models (with more hidden units) are able to fit the training set better, until eventually the largest models overfit the data. \n",
    "- The best hidden layer size seems to be around n_h = 5. Indeed, a value around here seems to  fits the data well without also incurring noticeable overfitting.\n",
    "- You will also learn later about regularization, which lets you use very large models (such as n_h = 50) without much overfitting. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Optional questions**:\n",
    "\n",
    "**Note**: Remember to submit the assignment by clicking the blue \"Submit Assignment\" button at the upper-right. \n",
    "\n",
    "Some optional/ungraded questions that you can explore if you wish: \n",
    "- What happens when you change the tanh activation for a sigmoid activation or a ReLU activation?\n",
    "- Play with the learning_rate. What happens?\n",
    "- What if we change the dataset? (See part 5 below!)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='blue'>\n",
    "**You've learnt to:**\n",
    "- Build a complete neural network with a hidden layer\n",
    "- Make a good use of a non-linear unit\n",
    "- Implemented forward propagation and backpropagation, and trained a neural network\n",
    "- See the impact of varying the hidden layer size, including overfitting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nice work! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5) Performance on other datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want, you can rerun the whole notebook (minus the dataset part) for each of the following datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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n1FlkHUkkpHld2r9yL3UGdCi03awjiUXKFCgOJwc++4Mu7zxYpnew213EbzxN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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f179cbc13c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Datasets\n",
    "noisy_circles, noisy_moons, blobs, gaussian_quantiles, no_structure = load_extra_datasets()\n",
    "\n",
    "datasets = {\"noisy_circles\": noisy_circles,\n",
    "            \"noisy_moons\": noisy_moons,\n",
    "            \"blobs\": blobs,\n",
    "            \"gaussian_quantiles\": gaussian_quantiles}\n",
    "\n",
    "### START CODE HERE ### (choose your dataset)\n",
    "dataset = \"noisy_moons\"\n",
    "### END CODE HERE ###\n",
    "\n",
    "X, Y = datasets[dataset]\n",
    "X, Y = X.T, Y.reshape(1, Y.shape[0])\n",
    "\n",
    "# make blobs binary\n",
    "if dataset == \"blobs\":\n",
    "    Y = Y%2\n",
    "\n",
    "# Visualize the data\n",
    "plt.scatter(X[0, :], X[1, :], c=Y, s=40, cmap=plt.cm.Spectral);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Congrats on finishing this Programming Assignment!\n",
    "\n",
    "Reference:\n",
    "- http://scs.ryerson.ca/~aharley/neural-networks/\n",
    "- http://cs231n.github.io/neural-networks-case-study/"
   ]
  }
 ],
 "metadata": {
  "coursera": {
   "course_slug": "neural-networks-deep-learning",
   "graded_item_id": "wRuwL",
   "launcher_item_id": "NI888"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
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